Abstract
Studies show that conventional factorization machines (FMs) have low performance in capturing both local and global structures of user–item correlation simultaneously. Recently, deep neural networks (DNNs) have been applied to improve FMs. However, DNNs increase the complexity of the training process. Moreover, DNN-based FMs ignore the integration of neighborhood-based approaches. An efficient method called factorization machine model via probabilistic auto-encoders (AutoFM) is proposed to resolve this issue in the present study. The proposed AutoFM can extract non-trivial and local structures characteristics from user–user/item–item co-occurrence pairs by integrating a low-complexity probabilistic auto-encoder. Furthermore, it supports both explicit and implicit feedback datasets. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed method. The results show that the AutoFM outperforms the current state-of-the-art methods in rating prediction tasks. Compared with the DNN-based FM models, the proposed AutoFM model improves the item ranking at least 1.16%\(\sim\) 4.37%.
Similar content being viewed by others
References
Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: The 41st International ACM SIGIR Conference on research and development in information retrieval, pp 515–524
Rendle S (2010) Factorization machines, in: 2010 IEEE International conference on data mining. IEEE, pp 995–1000
Rendle S (2012) Factorization machines with libfm. Acm Trans Intell Syst Technol 3(3):1–22
Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: ACM SIGKDD International Conference on knowledge discovery and data mining, pp 426–434
Koren Y (2010) Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1–24
He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355–364
Liu C, Zhang T, Zhao P, Zhou J, Sun J (2017) Locally linear factorization machines. In: Proceedings of the 26th International joint conference on artificial intelligence, pp 2294–2300
Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint. arXiv:1703.04247
Bengio Y (2009) Learning deep architectures for ai, Foundations & Trends®. Mach Learn 2(1):1–127
Zhuang F, Zhang Z, Qian M, Shi C, Xie X, He Q (2017) Representation learning via dual-autoencoder for recommendation. Neural Netw 90:83–89
Huang T, Zhang D, Bi L (2020) Neural embedding collaborative filtering for recommender systems. Neural Comput Appl 32(22):17043–17057
Zhang S, Yao L, Tay Y, Xu X, Zhang X, Zhu L (2018) Metric factorization: recommendation beyond matrix factorization. arXiv preprint. arXiv:1802.04606
Mikolov T, Sutskever I, Kai C, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Proc Syst 26:3111–3119
Landgraf AJ, Bellay J, Word2vec skip-gram with negative sampling is a weighted logistic pca, arXiv preprint arXiv:1705.09755
Ozsoy MG, From word embeddings to item recommendation, arXiv preprint arXiv:1601.01356
Barkan O, Koenigstein N (2016) Item2vec: neural item embedding for collaborative filtering. In: Machine learning for signal processing (MLSP), 2016 IEEE 26th international workshop on, IEEE, pp 1–6
Liang D, Altosaar J, Charlin L, Blei DM (2016) Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems, pp 59–66
Liang H, Baldwin T (2015) A probabilistic rating auto-encoder for personalized recommender systems. In: Proceedings of the 24th ACM International on conference on information and knowledge management, pp 1863–1866
Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on research and development in Information Retrieval, pp 635–644
Baltrunas L, Church K, Karatzoglou A, Oliver N, Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild, arXiv preprint arXiv:1505.03014
Mingdang Tang ZZ, Zhang Tingting (2018) Qos-aware web service recommendation based on factorization machines. Chin J Comput 41(6):114–127
Da C, He X, Nie L, Wei X, Chua TS (2017) Cross-platform app recommendation by jointly modeling ratings and texts. Acm Trans Inf Syst 35(4):1–27
Oentaryo RJ, Lim EP, Low JW, Lo D, Finegold M (2014) Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: Acm International conference on web search and data mining
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791–798
Lee J, Kim S, Lebanon G, Singer Y, Bengio S (2016) Llorma: Local low-rank matrix approximation. J Mach Learn Res 17(1):442–465
Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp 111–112
Berg Rvd, Kipf TN, Welling M, Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295
Kingma DP, Ba J, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, MA, pp 1–35
Kabbur S, Ning X, Karypis G (2013) Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 659–667
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering, in: Proceedings of the 26th international conference on world wide web, pp 173–182
Haijun Zhang, Yanfang Sun, Mingbo Zhao, Tommy WSC and, Bridging user interest to item content for recommender systems: an optimization model., IEEE transactions on cybernetics
Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE transactions on industrial informatics 1–1
Castells P, Wang J, Lara R, Zhang D (2014) Introduction to the special issue on diversity and discovery in recommender systems. ACM Trans Intell Syst Technol 5(4):1–3
Clarke CL, Kolla M, Cormack GV, Vechtomova O, Ashkan A, Büttcher S, MacKinnon I (2008) Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, pp 659–666
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fully documented templates are available in the elsarticle package on CTAN.
Rights and permissions
About this article
Cite this article
Huang, T., Bi, L., Wang, N. et al. AutoFM: an efficient factorization machine model via probabilistic auto-encoders. Neural Comput & Applic 33, 9451–9466 (2021). https://doi.org/10.1007/s00521-021-05705-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-05705-4